grtrnorm: Sample from multiple multivariate normal distributions

In grt: General Recognition Theory

Description

Generate one or more samples from the two or more specified multivariate normal distributions.

Usage

grtrnorm(n,
        np = 2,
        means = list(rep(0,np), rep(0,np)), 
        covs = diag(rep(1,np)), 
        clip.sd = Inf,
        tol = 1e-6,
        empirical = TRUE, 
        seed = NULL,
        response.acc = NULL)

Arguments

n	the number of samples per population required
np	the number of populations to be sampled from
means	a list of vectors specifying the means of the variable for each populations
covs	a matrix or a list of matrices specifying the covariance matrices of the variables. Each matrix should be positive-definite and symmetric.
clip.sd	an integer specifying the cutoff value of standard score. The standard score of a generated sample exceeding this value should be truncated. Default to Inf (no truncation).
tol	tolerance (relative to largest variance) for numerical lack of positive-definiteness in covs.
empirical	logical. If true, means and covs specify the empirical rather than population means and covariance matrices.
seed	an integer internally supplied as seed argument to the function set.seed. If NULL, .Random.seed is used.
response.acc	an optional numeric value between 0 and 1, specifying the classification accuracy of a hypothetical observer. See ‘Details’. Default to NULL.

Details

This function is essentially a wrapper to the mvrnorm function in MASS package.

If the optional response.acc argument is supplied, hypothetical random classification responses with specified accuracy will be generated.

Value

a data frame containing a column of numeric category labels and column(s) of sampled values for each variable, and optionally, a column of hypothetical responses.

Author(s)

Author of the original Matlab routines: Leola Alfonso-Reese

Author of R adaptation: Kazunaga Matsuki

References

Alfonso-Reese, L. A. (2006) General recognition theory of categorization: A MATLAB toolbox. Behavior Research Methods, 38, 579-583.

Examples

m <- list(c(268,157), c(332, 93))
covs <- matrix(c(4538, 4351, 4351, 4538), ncol=2)
II <- grtrnorm(n=80, np=2, means=m, covs=covs)


m <- list(c(283,98),c(317,98),c(283,152),c(317,152))
covs <- diag(75, ncol=2, nrow=2)
CJ <- grtrnorm(n=c(8,16,16,40), np=4, means=m, covs=covs)
CJ$category <- c(1,1,1,2)[CJ$category]

Example output

Loading required package: MASS

Attaching package: 'grt'

The following object is masked from 'package:base':

    scale

grt documentation built on May 2, 2019, 7:10 a.m.